A superpixel-based framework for automatic tumor segmentation on breast DCE-MRI

Ning Yu, Jia Wu, Susan P. Weinstein, Bilwaj Gaonkar, Brad M. Keller, Ahmed B. Ashraf, Yunqing Jiang, Christos Davatzikos, Emily F. Conant, Despina Kontos

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Accurate and efficient automated tumor segmentation in breast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is highly desirable for computer-aided tumor diagnosis. We propose a novel automatic segmentation framework which incorporates mean-shift smoothing, superpixel-wise classification, pixel-wise graph-cuts partitioning, and morphological refinement. A set of 15 breast DCE-MR images, obtained from the American College of Radiology Imaging Network (ACRIN) 6657 I-SPY trial, were manually segmented to generate tumor masks (as ground truth) and breast masks (as regions of interest). Four state-of-the-art segmentation approaches based on diverse models were also utilized for comparison. Based on five standard evaluation metrics for segmentation, the proposed framework consistently outperformed all other approaches. The performance of the proposed framework was: 1) 0.83 for Dice similarity coefficient, 2) 0.96 for pixel-wise accuracy, 3) 0.72 for VOC score, 4) 0.79 mm for mean absolute difference, and 5) 11.71 mm for maximum Hausdorff distance, which surpassed the second best method (i.e., adaptive geodesic transformation), a semi-automatic algorithm depending on precise initialization. Our results suggest promising potential applications of our segmentation framework in assisting analysis of breast carcinomas.

Original languageEnglish (US)
Title of host publicationMedical Imaging 2015
Subtitle of host publicationComputer-Aided Diagnosis
EditorsLubomir M. Hadjiiski, Lubomir M. Hadjiiski, Georgia D. Tourassi, Georgia D. Tourassi
PublisherSPIE
ISBN (Electronic)9781628415049, 9781628415049
DOIs
StatePublished - 2015
EventSPIE Medical Imaging Symposium 2015: Computer-Aided Diagnosis - Orlando, United States
Duration: Feb 22 2015Feb 25 2015

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume9414
ISSN (Print)1605-7422

Other

OtherSPIE Medical Imaging Symposium 2015: Computer-Aided Diagnosis
CountryUnited States
CityOrlando
Period2/22/152/25/15

Keywords

  • Breast DCE-MRI
  • Graph-cuts
  • Superpixel
  • Tumor Segmentation

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Biomaterials
  • Atomic and Molecular Physics, and Optics
  • Radiology Nuclear Medicine and imaging

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  • Cite this

    Yu, N., Wu, J., Weinstein, S. P., Gaonkar, B., Keller, B. M., Ashraf, A. B., Jiang, Y., Davatzikos, C., Conant, E. F., & Kontos, D. (2015). A superpixel-based framework for automatic tumor segmentation on breast DCE-MRI. In L. M. Hadjiiski, L. M. Hadjiiski, G. D. Tourassi, & G. D. Tourassi (Eds.), Medical Imaging 2015: Computer-Aided Diagnosis [94140O] (Progress in Biomedical Optics and Imaging - Proceedings of SPIE; Vol. 9414). SPIE. https://doi.org/10.1117/12.2081943